ChIP-chip/seq in combination with transcriptome profiling has greatly helped our understanding of the molecular mechanisms underlying many physiological and pathological processes. It has also left unanswered questions on the combinatorial and context-specific nature of mammalian transcription regulation, and created challenges for computational data integration and modeling. To address these challenges, we propose to: 1) develop the computational framework for constructing condition-specific combinatorial and probabilistic transcription regulatory modules in mammalian genomes by integrating transcription factor ChIP-chip/seq, cis-element epigenome and transcriptome data; 2) apply the model in 1) to construct a comprehensive probabilistic nuclear receptor regulatory network, experimentally validate the predictions, and use the results to refine the model; 3) develop and maintain an open source publicly available integrated ChIP-chip/seq data analysis pipeline Cistrome. With rapid growth of transcription factor ChIP-chip/seq, cis-element epigenome, and transcriptome datasets, our methods will integrate the available datasets, infer the important missing data, and extract maximum knowledge from individual datasets. Our resulting nuclear receptor regulatory network and computational tools will also be a good resource for the community.